Are there significant correlations between climate factors and the spread of COVID-19 for less densely populated and less polluted regions?

  1. Marcelo de Paula Corrêa
  2. Ana Letícia Campos Yamamoto
  3. Luiz Felipe Silva
  4. Ivana Riêra Pereira Bastos
  5. Tális Pereira Matias
  6. Raquel Gonçalves Pereira
  7. Patrícia Martins do Reis
  8. Flávia Fernanda Azevedo Fagundes
  9. Alysson Fernando Ribeiro
  10. Joaquim Augusto Cortez Moraes
  11. Filipe Pereira da Silva

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Abstract

This study analyzes the correlation between the spread of COVID-19 and meteorological variables (air temperature, relative humidity, wind speed, and precipitation) in urban-rural cities located in southeastern Brazil. Spearman’s correlation coefficients were used for the statistical analysis. Results show that air temperature and wind speed were positively correlated with COVID-19 cases, while air relative humidity showed negative correlation. As seen in several recent studies, climate factors and the spread of COVID-19 seem to be related. Our study corroborates this hypothesis for less densely populated and less polluted regions. We hope that our findings help worldwide scientific efforts towards understanding this disease and how it spreads in different regions.

Highlights

  • Climate and COVID-19’s spread were also correlated in less-densely populated regions.

  • Both maximum and minimum temperatures are strongly correlated with cases of covid-19.

  • One hypothesis for the strong association could be the high minimum temperatures in the subtropical region.

  • Wind speed is also positively correlated with COVID-19, while air humidity is negatively related.

  • Mitigation policies against the spread of COVID-19 should be based on local climate profiles.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.11.21251129: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: SigmaPlot 12.5 software (Systat Software Inc., USA) was used to calculate Spearman’s correlation coefficient for the six study locations.
    SigmaPlot
    suggested: (SigmaPlot, RRID:SCR_003210)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.02.11.21251129v1 on medRxiv